Surface Roughness Estimation for Turning Operation Based on Different Regression Models Using Vibration Signals

Suleyman Neseli, Gokhan Yalcin, suleyman Yaldiz
  • Gokhan Yalcin
    Konya Teknik University, Turkey
  • suleyman Yaldiz
    Selcuk University, Turkey

Abstract

On machined parts, major indication of surface quality is surface roughness and also surface quality is one of the most specified customer requirements. In the turning process, the importance of machining parameter choice is enhancing, as it controls the required surface quality. To obtain the better surface quality, the most essential control parameters are tool overhang and tool geometry in turning operations. The goal of this study was to develop an empirical multiple regression models for prediction of surface roughness (Ra) from the input variables in finishing turning of 42CrMo4 steel. The main input parameters of this model are tool overhang and tool geometry such as tool nose radius, approaching angle, and rake angle in negative direction. Regression analysis with linear, quadratic and exponential data transformation is applied so as to find the best suitable model. The best results according to comparison of models considering determination coefficient (R2) are achieved with quadratic regression model. In addition, tool nose radius was determined as the most effective parameter on turning by variance analysis (ANOVA). Cutting experiments and statistical analysis demonstrate that the model developed in this work produces smaller errors than those from some of the existing models and have a satisfactory goodness in all three models construction and verification.

Keywords

Cutting tool geometry; Regression analysis; Surface roughness; Tool-holder overhang; Turning

Full Text:

PDF
Submitted: 2018-07-13 16:14:04
Published: 2018-12-27 18:57:41
Search for citations in Google Scholar
Related articles: Google Scholar

References

Mishra, V., Khan, G.S., Chattopadhyay, K.D., Nand, K., and Sarepaka, R.G.V., “Effects of tool overhang on selection of machining parameters and surface finish during diamond turning”, Measurement 55, 353-361, (2014).

Kotkar, D.R., Wakchaure, V.D., “Vibration control of newly designed Tool and Tool-Holder for internal treading of Hydraulic Steering Gear Nut”, International Journal Of Modern Engineering Research 4(6), 46-57, (2014).

Hadi, Y., “Dynamic Deflection of Periodic Cutting Tool Holder Based on Passive Model”, International Journal of Mechanical & Mechatronics Engineering 11 No: 06, (2011).

Kiyak, M., Kaner, B., Sahin, I., Aldemir, B., and Cakir, O., “The Dependence of Tool Overhang on Surface Quality and Tool Wear in the Turning Process”, International Advanced Manufacturing Technology 51(5), 431-438, (2010).

Sathishkumar, B., Mohanasundaram, K.M. and Senthilkumar, M., “Experimental studies on impact of particle damping on surface roughness of machined components in boring operation”, European Journal of Scientific Research 71(3), 327-337, (2012).

Kassab, S.Y., and Khoshnaw, Y.K., “The effect of cutting tool vibration on surface roughness of workpiece in dry turning operation”, International Journal of Engineering Science and Technology 25(7), 879-889, (2007).

Batey, M.C., and Hamidzadeh, H.R., “Turning process using vibration signature analysis”, Journal of Vibration and Control 13(5), 527-536, (2007).

Sardar, N., Bhaumik, A., Mandal, N.K., “Modal analysis and experimental determination of optimum tool shank overhang of a lathe machine”, Sensors & Transducers Journal 99(12), 53-65, (2008).

Amin, A.K.M.N., Nashron, F.R. and Zubaire, W.W.D., “Role of the frequency of secondary serrated teeth in chatter formation during turning of carbon steel AISI 1040 and stainless steel”, Proceeding 1st International Conference & 7th AUN/SEED-Net, Field-wise Seminar on Manufacturing and Material Processing, 181-186, (2006).

Haddadi, E., Shabghard, M.R. and Ettefagh, M.M., “Effect of different tool edge conditions on wear detection by vibration spectrum analysis in turning operation”, Journal of Applied Sciences 8(21), 3879-3886, (2008).

Abouelatta, O.B. and Mádl, J., “Surface roughness prediction based on cutting parameters and tool vibrations in turning operations”, Journal of Materials Processing Technology 118, 269-277, (2001).

Özel, T. and Karpat, Y., “Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks”, International Journal of Machine Tools & Manufacture 45, 467-479, (2005).

Adalarasan, R., Santhanakumar, M. and Rajmohan, M., “Optimization of laser cutting parameters for Al6061/SiCp/Al2O3 composite using grey based response surface methodology (GRSM)”, Measurement, 73, 596-606, (2015).

Debnath, S., Reddy, M.M. and Yi, Q.S., “Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method”, Measurement, 78, 111-119, (2016).

Asiltürk, İ., Neşeli, S. and İnce, M.A., “Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods”, Measurement, 78, 120-128, (2016).

Singh, D. and Rao, P.W., “A Surface roughness prediction model for hard turning process”, International Journal of Advanced Manufacturing Technology, 32(11–12), 1115–1124, (2007).

Zain, A.M., Haron, H., Qasem, S.N. and Sharif, S., “Regression and ANN models for estimating minimum value of machining performance”, Applied Mathematical Modelling, 36, 1477-1492, (2012).

Arbizu, P.I. and Pérez, C.J.L., “Surface roughness prediction by factorial design of experiments in turning processes”, Journal of Materials Processing Technology, 143–144, 390-396, (2003).

Thiele, J.D., Melkote, S.N., Peascoe, R.A. and Watkins, T.R., “Effect of cutting-edge geometry and workpiece hardness on surface residual stresses in finish hard turning of AISI 52100 steel”, ASME Journal of Manufacturing Science and Engineering, 122, 642-649, (2000).

Montgomerty, D.C., “Design and Analysis of Experiments”, eighth ed., John Willey&Sons Inc., New York, (2013).

Asilturk, I., Celik, L., Canlı, E., and Onal, G., “Regression Modeling of Surface Roughness in Grinding”, Advanced Materials Research, 271(273), 34-39, (2011).

Tasdemir, S., Urkmez, A., and Inal, S., “Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis”, Computers and Electronics in Agriculture, 76, 189-197, (2011).

Abstract views:
89

Views:
PDF
68




Copyright (c) 2018 International Journal of Intelligent Systems and Applications in Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
 
© Prof.Dr. Ismail SARITAS 2013-2019     -    Address: Selcuk University, Faculty of Technology 42031 Selcuklu, Konya/TURKEY.